Abstract
We consider the discovery of future research collaborations as a link prediction problem applied on scientific knowledge graphs. Our approach integrates into a single knowledge graph both structured and unstructured textual data through a novel representation of multiple scientific documents. The Neo4j graph database is used for the representation of the proposed scientific knowledge graph. For the implementation of our approach, we use the Python programming language and the scikit-learn ML library. We benchmark our approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Our initial experimentations demonstrate a significant improvement of the accuracy of the future collaboration prediction task. The experimentations reported in this paper use the new COVID-19 Open Research Dataset.
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Acknowledgments
The work presented in this paper is supported by the OpenBio-C project (www.openbio.eu), which is co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (Project id: T1EDK- 05275). The authors would also like to thank Stamatis Karlos for his assistance with the statistical analysis of the data.
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Giarelis, N., Kanakaris, N., Karacapilidis, N. (2020). On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: A Link Prediction Perspective. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_29
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